characterization and delineation of farming situations of

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Journal of Pharmacognosy and Phytochemistry 2018; 7(4): 2208-2214

E-ISSN: 2278-4136

P-ISSN: 2349-8234

JPP 2018; 7(4): 2208-2214

Received: 19-05-2018

Accepted: 21-06-2018

Sameer Mandal

SV College of Agriculture

Engineering and Technology,

Indira Gandhi Agricultural

University, Raipur,

Chhattisgarh, India

D Khalkho




University, Raipur,

Chhattisgarh, India

MP Tripathi




University, Raipur,

Chhattisgarh, India

Love Kumar




University, Raipur,

Chhattisgarh, India

SD Kushwaha

Chhattisgarh State Watershed

Management Agency

(CGSWMA), Raipur,

Chhattisgarh, India

Correspondence

Sameer Mandal




University, Raipur,

Chhattisgarh, India

Characterization and delineation of farming

situations of Durg district

Sameer Mandal, D Khalkho, MP Tripathi, Love Kumar and SD Kushwaha

Abstract

The soil is the most vital and precious natural resource that sustains life on the earth. The native ability of

soils to supply sufficient nutrients has decreased with higher plant productivity levels, associated with

increased human demand for food. The study area is an increase in soil depth, water holding capacity,

cation exchange capacity and preponderance of calcium and magnesium ions. Land and water both are

the limiting resources of living thing so that it is required to understand their importance and efficient

use. Remote sensing and GIS is the advance tool to investigate the land resources and make plan by

policy makers for its better utilization. Farming situation tells about the land suitability for cultivation.

The Multi Criteria Evaluation (MCE) approach is used for the characterization of farming situation.

Various thematic maps such as NDSI, slope, LULC and texture map was used for the characterization

and delineation of farming situation. Multi Criteria Evaluation (MCE) approach was applied to estimate

different farming situation through assigning the weights of indicators and sub-indicators into account

their influences in selecting suitable farming situation. Based on that the four orders of farming situation

were categorized such as Bhata, Matasi, Dorsa and Kanhar. The area under Matasi is highest 1028.20 sq

km (43.59%) among all of them followed by Dorsa 957.92 sq km (40.61%) and Kanhar 214.39 sq km

(9.09%). Whereas Bhata having least area 120.97 sq km (5.13%). Another class of water body was also

separately classified and it was found that 37.18 sq km (1.58%), in the study area.

Keywords: Farming situation, NDSI, MCE, overlay analysis, physiochemical properties

Introduction

Soil is the most important economic industry for the millions of people in rural areas. For

decades, soil has been associated with the production of vital crops, herbs, raw materials and

variety of human needs for sustainable development (Brady and Weil, 2007). Crop production

depends fully on quality of soil and its fertility (Lal, 1998). It is the highest priority of soil

science to improve and promote the understanding of soil and it function for economic crop

production (Brady and Weil, 2007). The achievable objective is providing adequate and

reliable soil information on how to protect, restore and manage soil resources on agricultural

land for high and healthy crop yield, globally. Hence, the role of soil and its applied science is

of utmost important for sustainable crop production and economic development.

Land use maps should be prepared, and land classes should be determined so as to decrease the

negative effects of human activities on nature. Satellite images are important in these studies.

Analysing satellite images on computers with GIS program, and determining the condition of

the land is important process for land classification (Panhalkar, 2011). Land classes, and

locations of agricultural activities, settlement conditions, irrigation possibilities, industrial

activities are chosen according to these land classes (Clawson and Stewart, 1965; Burley,

1961; Anderson et al. 1972; Jahantigh and Efe, 2010).

Materials and Methods

Details of the study area

Chhattisgarh is located in the centre-east of the country, and stretches across the latitudinal

expanse of 17°46' to 23°15' North on one hand to the longitudinal meridian of 80°30' to 84°23'

East on the other. The climate of Chhattisgarh is tropical. It is hot and humid because of its

proximity to the Tropic of Cancer and its dependence on the monsoons for rains. Durg is the

part of the state. The monsoon season is from late June to October and is a welcome respite

from the heat. Chhattisgarh receives an average of 1,292 millimetres of rain. Area of Durg

district is 2356.6 sq km. location map of the study area is shown in Fig. 1.

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Journal of Pharmacognosy and Phytochemistry

Fig 1: Location map of the study area

Soil characteristics

Soils of durg mainly developed by the action and interactions

of relief, parent material and climate. Biotic features, mainly

the natural vegetation follows the climatic patterns. Area of

Durg comes under the soil orders of Entisol, Inceptisols,

Alfisols, and Vertisols. The local studies as per the farmers

understanding are known as Bhata, Matasi, Dorsa and

Kanhar respectively.

Land use pattern

Rice and wheat are the major crops of the state of

Chhattisgarh with yield of 1455 and 1024 kg/ha which is less

than the National average of respective crops. Though the

productivity level of al1 food grains in the state is lower than

the National average, but the total production of food grains

in the state is more than the State’s requirement.

Data Acquisition

As a part of this project, various data were acquired from

different sources as mentioned below:

Soil data: Field survey, soil health card portal & GIS

Cell, Chhattisgarh State Watershed Management Agency,

Govt. of C.G., Raipur.

Water quality data: Central Ground Water Board, NCCR,

Raipur.

Satellite data: https://www.usgs.gov

Soil data

For evaluation of the soil fertility status of study area, random

soil sampling points based on four class distribution of

satellite data using unsupervised classification technique was

carried out. Surface 0-30 (cm depth) soil samples were

collected from the different parts of Durg district of

Chhattisgarh Plains using GPS (Global Positioning System)

marked locations.

Digital Elevation Model

Digital Elevation Model (DEM) is sampled array of

elevations (z) that are spaced at regular intervals in the x and

y directions. The digital elevation data was downloaded from

the website of United States Geological Survey (USGS) and

shown in Fig.2. The Shuttle Radar Topography Mission

(SRTM) data was available as 1 arc second (approx. 30m

resolution) DEMs for the study area. Before using the

downloaded DEM, it is required to apply the geometric

correction. Therefore, the SRTM DEM was re-projected to

Universal Transverse Mercator (UTM) co-ordinate system

with Datum WGS 1984 (Zone-44) with spatial resolution of

30 m.

Satellite data

Cloud Free Landsat satellite data of year 2017 of the study

area was downloaded from the official website of USGS

(www.earthexplorer.gov.in) and shown in Fig.3. All the data

were pre-processed and projected to the Universal Transverse

Mercator (UTM) projection system with WGS 84 datum

(zone-44). The details of the satellite data collected and band

designation are given in the Table. The spatial resolution of

Landsat-8 is 30m (Visible, NIR, and SWIR); 100 meters

(thermal); 15 meters (panchromatic) with 16 days of temporal

resolution.

Table 1: Technical specification of satellite data

Date of

acquisition Satellite/Sensor

Reference

system Path/Row

Resolution

(m)

November

2017 Landsat-8

World Wild

Reference

System –II

(WRS-II)

142/45,

142/46,

143/45

30 m

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Fig 2: Digital Elevation Model of the study area Fig 3: Landsat-8 (2017) Satellite imagery of the study area

Data Processing

Soil data

Creating a geodatabase for the study area from the point data

based on field survey and soil health card. It comprises soil

texture, soil depth and bulk density.

Normalised Difference Soil Index (NDSI)

Soil water content or soil moisture is an important link

between the exchange of water and energy at the soil-

atmosphere interface (Richard et al. 2006). NDSI has the

potential to detect soil water content because shortwave

infrared wavelength can observe the moisture condition on the

ground. Normally, the optical satellite image can detect the

surface conditions, not underground condition, because it

cannot penetrate through the objects. However, the results

showed that NDSI has the best correlation with soil water

content at 10 cm depth because it has the similar condition

with the top soil surface layer. For soil water content at 5 cm

depth, even it is closer to soil surface than 10 cm depth; it is

the uncertain soil layer. Mostly, the water passes through this

layer and starts to keep the water at 10 cm depth (Potithep et

al. 2009).

Output of the NDSI method creates a single-band dataset that

only shows Soil water content. Values close to zero represent

rock and bare soil where negative values represent water,

snow and clouds. Taking ratio or difference of two bands

makes the vegetation growth signal differentiated from the

background signal. By taking a ratio of two bands drop the

values between -1 to +1. Forest has an NDSI value less than -

0.1 and bare soils have NSDI between -0.1 and -5. Increase in

the positive NDSI value means greater of soil moisture. The

NDSI was calculated from reflectance measurements in the

NIR and short wave infra-red (SWIR) portion of the spectrum

with the help of Eq. 1(Potithep et al. 2009).

𝑁𝐷𝑆𝐼 =𝑆𝑊𝐼𝑅−𝑁𝐼𝑅

𝑆𝑊𝐼𝑅+𝑁𝐼𝑅 (1)

Slope map

The slope map was prepared from Digital elevation model of

30 m resolution. The DEM was required to define the

projection to WGS 84, Zone-44 datum system. After defining

the projection of DEM, slope map was generated using this

process: Toolboxes > Spatial Analyst Tools > Surface > Slope

in per cent. The slope map was reclassified for further

processing of generation of farming situation map.

Soil Texture map

Soil texture map was generated from the date of the texture

analysis from department of soil science, IGKV, Raipur. The

geodatabase of soil texture was created and map of soil

texture was generated.

Land use/cover

Land use/cover classification is the process of sorting pixels

into a finite number of individual classes, or categories of data

based on their data file values. There are two ways to classify

pixels into different categories. First technique i.e. supervised

classification is more closely with the real values than

unsupervised classification. In this process, pixels that

represent patterns recognize or can identify with help from

other sources. Knowledge of the data, the classes desired, and

the algorithm to be used is required before selecting training

samples. By identifying patterns in the imagery the computer

system to identify pixels with similar characteristics. By

setting priorities to these classes, supervise the classification

of pixels as they are assigned to a class value. If the

classification is accurate, then each resulting class

corresponds to a pattern that you originally identified. The

ERDAS IMAGINE 2016 software has been used for land

use/cover classification in this study. Most common land use

classification method “supervised classification” also known

as pixel based classification was used. The land use classes

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Journal of Pharmacognosy and Phytochemistry found in the study area were forest, agriculture land, current

fallow, barren land, settlement and water body.

Multi Criteria Evaluation (MCE)

The Characterization of farming situation map consists of

three stages. In the first stage, Soil texture map have been

converted to digital format by digitization. Remote sensing

data already have in digital format. The second stage involves

generation of thematic maps such as slope map and Land

use/cover map from different sources. It involves digital

image processing of remote sensing data. The third stage

involves the integration and generation of the data in a GIS

environment.

The ERDAS Imagine and ArcGIS software were used in

different stages of the work. The weightages of individual

themes and feature score were fixed and added to each layers

depending on their suitability of farming. This process

involves raster overlay analysis and is known as Multi

Criteria Evaluation (MCE) techniques. Multi criteria based

analysis has been adopted for categorization of land based on

their suitability of farming. The generated soil texture map is

in vector format, for Weighted Overlay Analysis (WOA)

these format was to convert into raster format, which is

known as “Rasterization”. The rasterization is performed for

converting different lines and polygon coverage into raster

data format. After this, reclassification of all the raster files is

processed along with providing the scale value of each unit.

The process of characterization of farming situations is given

in Fig. 4. A scale value in the range 1 to 5 (5 was the highest

weightage order and 1 was the lowest weightage order) is

used. All the layers are given ranking based on their influence

on the study, Normalized Difference Soil Index (NDSI) map

was given 40% weightage, slope map was given 30%

weightage, texture map was given 20% and Land use/cover

(LULC) map was given 10% weightage. This weightage was

given based on the importance of a particular layer (Table

3.5). Further, in the Spatial Analyst Tool, Weighted Overlay

Function has been processed for delineate the farming

situation. Based on the Weighted Overlay Function a farming

situation map was given for the study area.

Fig 4: Land use/cover map

Table 2: Weightage criteria for calculating farming situation of Durg District

Raster % Influence (Weighted Assigned) Feature Classes Feature Weighted Scale Value

NDSI 40

Very low 1

Low 2

Medium 3

Moderately High 4

Slope 30

Level 4

Level gently 3

Undulating 2

Rolling 1

Soil Texture Map 20

Silty loam 4

Silty clay loam 4

Sandy loam 1

Sandy Clay Loam 1

Loam 2

Clay loam 3

Clay 4

LULC 10

Water body 5

Agriculture Land 4

Current Fallow 3

Barren Land 2

Settlement 1

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Fig 5: Process flow diagram for characterization of farming situation

Results and Discussion

Slope map

The elevation varies between 171 m to 286 m above MSL in

the study area. The highest elevation recorded in the

watershed is 286 m above MSL. Slope map of Durg as shown

in Fig.6. The slope map played major role in delineating the

farming situations. The land slope of an area is decides the

suitability of farming. Slope map of Durg district is shown in

Fig.6.

Normalized Difference Soil Index (NDSI) map

Normalized Difference Soil Index (NDSI) was calculated

using ArcGIS software and reclassify to four classes shown in

Table 4.26. The value ranges from -1 to -0.1 shows forest area

and -0.1 to -0.05 shows bare soil. With increase in value of

NDSI the soil moisture is also increase. The values greater

than 0.02 is defines the highest soil moisture area. NDSI map

of Durg Distric is given in Fig.7.

Table 3: NDSI classification range

S. No Classes NDSI Value range

1 Very Low <-0.1

2 Low -0.1 to -0.05

3 Moderately Low -0.05 to 0

4 Medium 0 to 0.02

5 Moderately High >0.02

Fig 6: Slope map of the study area Fig 7: NDSI map

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Journal of Pharmacognosy and Phytochemistry Land use/cover

The cloud free geocoded digital data of Landsat-8 was

obtained from the USGS website. The imagery, which covers

the watershed, was used in the study. Based on the result of

image classification and image characteristics, the major land

use/ cover classes were identified including forest (21.50%),

agricultural land (30.27%), current fellow (27.79%), barren

land (14.87%), settlement (4.11%) and water body (1.46%)

(Fig. 8 & Table 4). The land use/cover map of Durg District

was also prepared and shown in Fig. 8.

Table 4: Area covered in different LULC class

Class name Area (sq km) Area (%)

Forest 16138.64 21.50

Agriculture 22724.98 30.27

Current Fellow 20860.3 27.79

Barren Land 11163.79 14.87

Settlement 3084.253 4.11

Water Body 1098.57 1.46

Farming situation

Characterization of farming situation was based on the Multi

Criteria Evaluation (MCE). Various map which is influence

by the land suitability for farming or cultivation such as

normalized difference soil index (NDSI), slope, soil texture

and land use/cover map was overlay. Overlay was done based

one weightage of each map on their influence over farming

situation. A weighted criterion of overlay analysis for Durg

District is shown in Table5. On the basis of that weighted

criteria characterization of farming situation of whole Durg

was prepared. Farming situation map of the Durg District is

shown in Fig.9. Area under different farming situation of

Durg is calculated and shown in Table5. The area under

Matasi is highest 1028.20 sq km (43.59%) among all of them

followed by Dorsa 957.92 sq km (40.61%) and Kanhar

214.39 sq km (9.09%). Whereas Bhata having least area

120.97 sq km (5.13%). Another class of water body was also

separately classified and it was found that 37.18 sq km

(1.58%), in the study area.

Fig 8: Land use/cover map Fig 9: Farming situation map of the study area

Table 5: Area covered under different farming situation of Chhattisgarh Plains

Farming situation Soil Order Soil texture Soil depth (cm) Slope (%) Bulk density (gm cm-3) Area (sq km) Area (%)

Bhata Entisols Loamy Fine Sand to Silt Loam <30 5-8 1.71-1.85 120.97 5.13

Matasi Inceptisols Sandy Loam to Silt Loam 30-80 3-5 1.48-1.79 1028.20 43.63

Dorsa Alfisols Sandy Clay Loam to Clay Loam 80-150 2-3 1.28-1.62 955.88 40.56

Kanhar Vertisols Clay Loam to Clay >150 <2 1.22-1.58 214.39 9.10

Water body - - - - - 37.18 1.58

Conclusions

GIS technique for superimposition of various thematic maps

including NDSI, land use/cover map, slope and texture map

can be used for characterization of farming situation of

Chhattisgarh Plains region. Various physical properties of soil

was calculated, wherein the soil depth of Entisols is less than

30 cm, Inceptisols is 30 to 80 cm, Alfisols is 80 to 150 cm and

Vertisols is more than 150 cm was found. Farming situations

was delineated by overlapping of NDSI, slope, soil texture

and LULC map. Multi Criteria Evaluation (MCE) approach is

used for characterization of farming situation. The area under

Matasi is highest 1028.20 sq km (43.59%) among all of them

~ 2214 ~

Journal of Pharmacognosy and Phytochemistry followed by Dorsa 957.92 sq km (40.61%) and Kanhar

214.39 sq km (9.09%). Whereas Bhata having least area

120.97 sq km (5.13%). Another class of water body was also

separately classified and it was found that 37.18 sq km

(1.58%), in the study area.

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